Google Analytics $index - What it is and how to use it

One of the features of Google Analytics is the ability to view the $index value of a certain page, but do you know what it means and how to use it?

How $index is calculated

$index is calculated by looking at which pages are viewed on the way to a person making a purchase or completing a goal. The more unique views a page gets on the way to a purchase or goal, the higher its $index will be. I’d advise that you take a look at the definition from Google as it is more in depth and includes some nice visuals to help you understand it. Here is one of the visuals which is very useful:

Ecommerce Revenue + Total Goal ValueNumber of Unique Pageviews for Given Page

Remember, it doesn’t tell you exactly how much revenue a page earned, it just gives you a rough idea of how valuable that page is in your conversion funnel.

You must have two things setup in your Analytics profile for this data to be reported -

Goals tracking

The goals must have a value assigned to them

Limitations of the $ index metric

One of the problems with the $ index metric is that it can be skewed very easily, so you need to make sure that you filter the data accordingly to take into account any pages that can do this. Here are a few examples of pages that you may wish to filter out -

Checkout process steps on an ecommerce website

Thank you pages

Pages with less than x number of pageviews (relative to the size of your site)

I’ll show you how to filter out this unwanted data below.

Also, as with all metrics, you should not focus on this as a single metric to define the success of a page or website. You need to take into account all of the available metrics when deciding what action to take to improve your website.

Actionable ways to use the $index metric

See which pages are most valuable

1. Go to Content > Top Content

2. Sort by the $index column

3. Filter out pageviews below a certain amount, this depends on the size of your site but essentially you just want to get rid of any pages with very low views

4. If you have an ecommerce site, you will also want to filter out any pages related to the checkout process as these can skew the figures

You now have a list of which pages people most commonly visit on the way to your conversion page.

What you can use this data for

Compare and contrast high value vs low value pages

There is a really easy way to compare which pages are performing well against the site average and which ones are not doing well. After you have completed the process above, just look for this option:

When you have clicked on this, select $ index from the drop down menu, then you will see something like this:

This is very actionable data, the example above clearly highlights which pages are performing well below the site average. So we can take a look at those pages and try to work out why this is the case. Sometimes there is a logical explanation for pages having a very low $ index value, which you need to take into account. However you can still get some great insights into your content by using this simple method.

Finding which sections or categories contribute most to conversions

You can drill further into this data and filter out certain sections of your site, for example you could choose just to view a certain category of content or certain category of products. Here is the process for finding the value of sections of your website:

1. Go to Content > Content Drilldown:

2. Its now just a case of clicking on the sections of content that you want to drill deeper into

3. Once you are in the section of content you want to analyse, click on the Comparison option:

4. Select $ index from the drop down menu

5. You can now see a graph of how sections of content compare to each other in terms of $ index

Checking if a page is above or below the site average $ index

If you navigate to either a single page or a section of content using the methods above, you will see this on the right hand side:

This gives you a quick indicator of how a specific page or section performs in relation to the rest of the site. We’d advise a little caution here though as we’re not sure how the site average figure is calculated. We’re not convinced that $ index numbers can be averaged, so we’d advise caution on this point.

Other Uses of the $ index Metric

If I’m being honest, I have always struggled a little to come up with unique and meaningful uses of the $ index metric. Most of the data it reveals are generally available through other parts of analytics. So if you have any other uses for this metric, feel free to leave a comment below.

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